This study aimed to explore the effects of dominant and compliant personalities, on both flow experience and the external characteristics of flow experience. A total of 48 participants were recruited to play an online game and subsequently asked to recall the songs they had heard while they were playing the game. Eye blink rate was recorded. The results demonstrated that (1) the participant was immersed in the game more if he/she was relatively dominant or noncompliant; (2) the perceptions about the external environment declined remarkably while being in a flow state; and (3) eye blink rates decreased only when the flow happened at the beginning of the game, rather than throughout the whole process. The results suggested that gamers who tend to be dominant or noncompliant were more likely to experience flow. Eye blink rate and perceptions of the external environment could be objective indicators of flow experience.

We propose Parametric Gauss Reconstruction (PGR) for surface reconstruction from point clouds without normals. Our insight builds on the Gauss formula in potential theory, which represents the indicator function of a region as an integral over its boundary. By viewing surface normals and surface element areas as unknown parameters, the Gauss formula interprets the indicator as a member of some parametric function spaces. We can solve for the unknown parameters using the Gauss formula and simultaneously obtain the indicator function. Our method bypasses the need for accurate input normals as required by most existing non-data-driven methods, while also exhibiting superiority over data-driven methods since no training is needed. Moreover, by modifying the Gauss formula and employing regularization, PGR also adapts to difficult cases such as noisy inputs, thin structures, sparse or nonuniform points, for which accurate normal estimation becomes quite difficult. Our code is publicly available at https://github.com/jsnln/ParametricGaussRecon.

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